DocumentCode
1844572
Title
Cascade Linear SVM for Object Detection
Author
Song, Jinze ; Wu, Tao ; An, Ping
Author_Institution
Coll. of Mechatron. Eng. & Autom., Nat. Univ. of Defence Technol., Changcha
fYear
2008
fDate
18-21 Nov. 2008
Firstpage
1755
Lastpage
1759
Abstract
This paper develops a cascade of linear SVM classifiers for fast object detection. The learning problem of every node in the cascade structure is described as a new quadratic programming problem in the frame of SVM, which makes every linear classifier achieve very high detection rate but only moderate false positive rate. The real experiment shows that this method enjoys good generalization capacity and much fast speed compared with the traditional SVMs.
Keywords
image classification; learning (artificial intelligence); object detection; quadratic programming; support vector machines; cascade linear SVM classifier; machine learning; object detection; quadratic programming; Automation; Detectors; Educational institutions; Face detection; Los Angeles Council; Mechatronics; Object detection; Risk management; Support vector machine classification; Support vector machines; Cascade; object detection; support vector machine (SVM);
fLanguage
English
Publisher
ieee
Conference_Titel
Young Computer Scientists, 2008. ICYCS 2008. The 9th International Conference for
Conference_Location
Hunan
Print_ISBN
978-0-7695-3398-8
Electronic_ISBN
978-0-7695-3398-8
Type
conf
DOI
10.1109/ICYCS.2008.173
Filename
4709239
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